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Live Music Archive

Live Music Archive

Astral Engineering LLC

Music免费v1.16.0
App Store
评分

4.9

1,695 条评分

星级

★★★★★

最近更新

2026年4月27日

发布日期

2019年3月12日

更新内容

v1.16.0

Phish fans — rejoice. Phish.in is here. Stream a huge archive of live Phish shows right alongside the Internet Archive's collection of live music. See the music — a new music visualizer turns any show into a live light show that reacts to every note. Lossless FLAC audio is now supported — the best-sounding shows finally get the quality they deserve. Sleep timer — drift off without the music playing all night. Last.fm and Libre.fm scrobbling — track your listening history anywhere.

应用信息

开发者
Astral Engineering LLC
分类
Music
价格
免费
版本
1.16.0
App ID
1453343128

简介

Phish.in is here — stream a huge archive of live Phish shows alongside the Internet Archive's massive live music collection. Thousands of artists and concerts from the 1960s to today: Grateful Dead, Phish, Billy Strings, Goose, String Cheese Incident, and so many more. Discover — Search by artist, taper, venue, festival, date, or song. Browse recent shows or dive deep into the archive. Listen your way — Stream over Bluetooth and AirPlay, let Infinite Shuffle keep the music going, or hand off between devices seamlessly. Lossless FLAC audio supported. Set a sleep timer to drift off. Scrobble to Last.fm or Libre.fm. See the music — A music visualizer turns any show into a live light show that reacts to every note. Always have something to listen to — Download shows for offline play. Offline Refresh automatically swaps out finished shows for new ones, so your library stays fresh — even without a connection. Make it yours — Favorite artists, shows, tapers, and venues from anywhere in the app. Sync across devices. Track your listening history. Share concerts with friends. Terms and Conditions: https://livemusicarchive.app/terms Privacy Policy: https://livemusicarchive.app/privacy

下载量预测

专业 · 预览

预估总下载量

126K85K242K
保守估计乐观估计

974

低 / 月

1K

预估 / 月

3K

高 / 月

基于1,695 条评分
假设评分率1.4%
应用年龄87 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。